ITR: Foundations of Visual Search

ITR：视觉搜索的基础

• 批准号: 0427372
• 负责人: Alan Bovik
• 金额: --
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-12-01 至 2010-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427372&HistoricalAwards=false
• 关键词: ITR; Foundations; Visual; Search

Project AbstractThis study is directed towards developing flexible, general-purpose Visual Search systems capable of Searching for objects in real, cluttered environments. The research will include extensive psychophysical and physiological experiments on humans and primates that will prototype artificial systems that mimic this behavior. The goals of the study can be conveniently divided into four Aims:Aim 1: Develop and prototype a revolutionary camera gaze control device dubbed Remote High-Speed Active Visual Environment, or RHAVEN. RHAVEN will allow telepresent control of the gaze of a remote camera using eye movements as rapidly and naturally as if viewing the scene directly.Aim 2: Develop optimal statistical bounds on Visual Search, by casting it as a Bayesian problem, yielding a maximum a posteriori (MAP) solutions for firstly, finding a target in a visual scene using a smallest number of fixations, and secondly, for next-fixation selection given a current fixation.Aim 3: Construct models for Visual Search based on Natural Scene Statistics at the point of gaze. Visually important image structures can be inferred by analyzing the statistics of natural scenes sampled by eye movements and fixations.Aim 4: Conduct neurophysiological studies on awake, behaving primates during Visual Search tasks. Measure and analyze search performance in awake, behaving monkeys, while measuring the responses of neural populations in the brain's frontal eye fields (FEF) which help control saccadic eye movements.Broader Impact: The results of this research should significantly impact numerous National Priorities: Searching Large Visual Databases, Robotic Navigation, Security Imaging, Biomedical Search, Visual Neuroscience, and many others. It is easy to envision scenarios that would benefit by a fundamental theory of Visual Search. For example: searching for suspect faces in airport security systems; examining internet streams for questionable material; semi-automatic search for lesions in mammograms; steering robotic vehicles around obstacles in hostile environs; navigating huge visual data libraries, etc.

项目摘要本研究旨在开发灵活的，通用的视觉搜索系统，能够在真实的，杂乱的环境中搜索对象。这项研究将包括对人类和灵长类动物进行广泛的心理物理和生理实验，这些实验将模拟这种行为的人工系统原型。这项研究的目标可以方便地分为四个目标：目标1：开发和原型一个革命性的相机凝视控制设备，称为远程高速主动视觉环境，或RHAVEN。RHAVEN将允许使用眼球运动对远程摄像机的凝视进行远程呈现控制，就像直接观看场景一样快速和自然。目标2：开发视觉搜索的最佳统计界限，通过将其转换为贝叶斯问题，产生最大后验（MAP）解决方案，首先，使用最少数量的注视在视觉场景中找到目标，其次，目标3：基于注视点处的自然场景统计构建视觉搜索模型。视觉上重要的图像结构，可以推断通过分析的眼动和fixations.Aim 4采样的自然场景的统计数据：清醒，行为灵长类动物在视觉搜索任务进行神经生理学研究。测量和分析清醒的猴子的搜索性能，同时测量大脑额叶眼区（FEF）的神经群体的反应，这有助于控制扫视眼球运动。更广泛的影响：这项研究的结果应该会对许多国家优先事项产生重大影响：搜索大型视觉数据库，机器人导航，安全成像，生物医学搜索，视觉神经科学等。很容易想象出视觉搜索的基本理论会带来什么样的好处。举例来说：在机场安全系统中搜索可疑的面孔;检查互联网流中的可疑材料;半自动搜索乳房X线照片中的病变;在敌对环境中驾驶机器人车辆绕过障碍物;导航巨大的视觉数据库等。